1. Field of the Invention
The present invention relates to a pathological diagnosis support device, and in particular, to a pathological diagnosis support device, pathological diagnosis support program, pathological diagnosis support method, and pathological diagnosis support system in which a pathological tissue to be diagnosed is extracted from a pathological image for diagnosis thereof.
2. Description of the Prior Art
In a clinical site, subtle extraordinary events can be detected thanks to development of diagnosis support devices using images such as images produced by x-ray photography, computer tomography (CT), and magnetic resonance imaging (MRI). However, since the devices produce only positional information of foreign substances, properties of the foreign substances cannot be identified. In consequence, a pathological expert observes by a microscope the foreign substances detected by the diagnosis device and determines malignity or benignity of the substance according to her or his phathological experiences. Therefore, for an example of the diagnosis in a vague boundary zone, the diagnostic result varies between the pathologists.
Specifically, in a pathological inspection, a tissue collected from an organ is dehydrated to be fixed using paraffin through a blocking process. The block is sliced into sections each having a thickness of five microns to eight microns, and then paraffin is removed from the sections. The section is stained to be observed by a microscope. The pathologist checks an image created by the microscope to diagnose the foreign substance on the basis of morphological information such as changes in size and shape of cellular nuclei and changes in patterns as tissues.
Japanese Patent Application Laid-Open No. 10-197522 describes a pathological tissue diagnosis support device in which a feature of an image obtained from a tissue image of a tissue of an organ is quantitatively represented as a feature quantity. Diagnosis categories beforehand set according to a pathological histological feature are employed as similarity calculation categories to calculate degrees of similarity of the feature quantity of the tissue image with respect to the similarity calculation categories. Resultantly, names of similarity calculation categories having a high degree of similarity are displayed.
Japanese Patent Application Laid-Open No. 2001-101418 proposes a feature extraction device in which a learning pattern as a discrimination object is projected onto a group of subspaces, and a square of length of the projection of the learning pattern onto each subspace is calculated as a feature vector. The device updates basic vectors of each subspace of the subspace group to increase a ratio between an inter-class variation and an intra-class variation of each element of the feature vector. As a result, the feature extraction device is stable against pattern variations and is suitable to discriminate patterns.
Japanese Patent Application Laid-Open No. 2003-256839 describes a pattern feature selection method, a pattern feature classification method, a pattern feature determination method, a program for the pattern feature selection, classification, and determination, and a device for the pattern feature selection, classification, and determination to implement high-performance pattern discrimination without requiring quite a large amount of learning.
NEC Technical Report Vol. 56, No. 10 published on Nov. 25, 2003 describes an automatic cancer cell extraction technique in which using pathological images, patterns of constituent elements of tissues such as a gland and interstitium, i.e., a connective tissue of glands are learned for detection thereof. As a result, it is possible to determine malignity of cancer with high accuracy.
However, the above inventions are attended with problems as follows.
According to the device described in Japanese Patent Application Laid-Open No. 10-197522, a neural network is adopted to calculate the similarity. That is, the similarity can be automatically calculated with high accuracy through a learning process only by preparing learning data. The learning data increases as the number of samples used by the device becomes greater. This improves the accuracy in the calculation of similarity. However, since the operation requires a considerably large volume of learning data, a long period of time is consumed to produce the similarity with high reliability.
In the methods of extracting patterns described in Japanese Patent Application Laid-Open Nos. 10-197522, 2001-101418, and 2003-256839 and NEC Technical Report Vol. 56, No. 10, it is possible to detect a feature, e.g., a tumor in the image obtained from the texture image of a living body. However, since particular situations inherent to the pathological image are not taken into consideration in the extraction of the feature from the pathological image, the part of the image indicating the feature cannot be detected with high precision.